Effective charging techniques must consider factors such as charging efficiency, lifecycle, charging time (CT), and battery temperature. Currently, most charging strategies primarily focus on CT and charging losses (CL), overlooking the crucial influence of battery temperature on battery life. Therefore, this study proposes a constant temperature–constant voltage (CT-CV) charging method based on minimizing energy losses. The charging process is primarily divided into three stages. In the first stage, a constant current (CC) charging is implemented using a 2C rate that aims to expedite battery charging but may result in a rapid temperature increase. The second stage involves constant temperature charging, where the charging current is regulated based on battery temperature feedback using a PID controller to maintain a stable battery temperature. The third stage is constant voltage (CV) charging, where a fixed current is applied continuously until the current drops below the charging cutoff current. After completion of the charging process, the charging time can be calculated, and charging losses can be determined by incorporating the battery equivalent circuit model (ECM). To determine the optimal transition time, the paper employs Coulomb counting and the battery ECM, considering both CT and losses to simulate the transition time with minimal CL. This approach achieves optimization of transition points by establishing ECM, measuring internal impedance of the battery, and simulating various charging scenarios, and eliminates the need for multiple actual experiments. Experimental results show that the charging time (CT) should be reduced and the maximum temperature rise (TR) should be reduced under the same average TR condition of the proposed method. At the same CT, the average TR and the maximum TR should both decrease. The charging method proposed in this study exhibits the following advantages: (1) simultaneous consideration of the battery’s equivalent circuit model and charging time; (2) the achieved transition point demonstrates characteristics of minimized charging losses; (3) eliminates the need for multiple experimental iterations.

Cruciferous vegetables are popular in Asian dishes. However, striped flea beetles prefer to feed on leaves, which can damage the appearance of crops and reduce their economic value. Due to the lack of pest monitoring, the occurrence of pests is often irregular and unpredictable. Regular and quantitative spraying of pesticides for pest control is an alternative method. Nevertheless, this requires manual execution and is inefficient. This paper presents a system powered by solar energy, utilizing batteries and supercapacitors for energy storage to support the implementation of edge AI devices in outdoor environments. Raspberry Pi is utilized for artificial intelligence image recognition and the Internet of Things (IoT). YOLOv5 is implemented on the edge device, Raspberry Pi, for detecting striped flea beetles, and StyleGAN3 is also utilized for data augmentation in the proposed system. The recognition accuracy reaches 85.4%, and the results are transmitted to the server through a 4G network. The experimental results indicate that the system can operate effectively for an extended period. This system enhances sustainability and reliability and greatly improves the practicality of deploying smart pest detection technology in remote or resource-limited agricultural areas. In subsequent applications, drones can plan routes for pesticide spraying based on the distribution of pests.

Sepsis is a life-threatening condition caused by an excessive immune response to infection, and even a one-hour delay in treatment can result in irreversible organ damage and increased mortality. This study aimed to develop an interpretable and efficient machine learning-based system for early sepsis prediction using routinely collected electronic health record (EHR) data. The research question focused on whether high predictive performance could be achieved using only a minimal set of clinical features. Data were obtained from intensive care units and general wards in the PhysioNet Computing in Cardiology Challenge 2019 dataset. Thirty-seven predefined clinical features were extracted and systematically analyzed to assess their predictive contributions. Several machine learning models were trained and evaluated using area under the receiver operating feature curve (ROC-AUC) and accuracy metrics. The proposed model achieved an ROC-AUC of 0.929 and an accuracy of 0.926 when using all features. Remarkably, comparable performance was maintained (ROC-AUC = 0.912, accuracy = 0.907) when only 10 carefully selected features were used. The system outperformed existing state-of-the-art approaches while relying solely on commonly available clinical parameters. An interpretable, feature-efficient sepsis prediction system was successfully developed, demonstrating strong performance with minimal data requirements. The approach is well-suited for resource-limited healthcare settings, such as rural hospitals, and has the potential to reduce diagnostic burden while enabling timely intervention to improve patient outcomes.